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Agricultural Policy Report APD/APR/01/2018

Efficiency and Profitability of Ukrainian Crop Production

Dr.Marten Graubner,

Research Associate, IAMO

Igor Ostapchuk

PhD Student, IAMO

Kiew, December 2017

About the Project “German-Ukrainian Agricultural Policy Dialogue” (APD)

The project German-Ukrainian Agricultural Policy Dialogue (APD) started 2006 and is

supported up to 2018 by the Federal Ministry of Food and Agriculture of Germany

(BMEL). On behalf of BMEL, it is carried out by the mandatary, GFA Consulting Group

GmbH, and a working group consisting of IAK AGRAR CONSULTING GmbH (IAK),

Leibniz-Institut für Agrarentwicklung in Transformationsökonomien (IAMO) and AFC

Consultants International GmbH. Project executing organization is the Institute of

Economic Research and Policy Consulting in Kyiv. The APD cooperates with the BVVG

Bodenverwertungs- und- verwaltungs GmbH on the implementation of key components

related to the development of an effective and transparent land administration system

in Ukraine. Beneficiary of the project is the Ministry of Agrarian Policy and Food of

Ukraine.

In accordance with the principles of market economy and public regulation, taking into

account the potentials, arising from the EU-Ukraine Association Agreement, the project

aims at supporting Ukraine in the development of sustainable agriculture, efficient

processing industries and enhancing its competitiveness on the world market. With

regard to the above purpose, mainly German, but also East German and international,

especially EU experience are provided by APD when designing the agricultural policy

framework and establishing of relevant institutions in the agriculture sector of Ukraine.

www.apd-ukraine.de

Authors:

Dr.Marten Graubner [email protected]

Igor Ostapchuk [email protected]

Disclaimer

This work is published under the responsibility of the German-Ukrainian Agricultural

Policy Dialogue (APD). Any opinions and findings, conclusions, suggestions or

recommendations expressed herein are those of the authors and do not necessarily

reflect the views of APD.

© 2017 German-Ukrainian Agricultural Policy Dialogue All rights reserved.

Content

List of abbreviations .................................................................................................................... 4

1. Introduction ....................................................................................................... 5

1.1. Motivation and statement of the research question ............................................ 5

1.2. Structure and development of crop production in Ukraine ................................. 6

2. Data and methods ................................................................................................................ 14

2.1. Dataset ...................................................................................................................... 14

2.2. Data envelopment analysis (DEA) ........................................................................ 16

2.3. Regression analysis ................................................................................................. 17

2.4. Treatment effect analysis ........................................................................................ 19

3. Results .................................................................................................................................... 20

3.1. Development of technical efficiency and total factor productivity .................... 20

3.2. Identification of important determinants of efficiency and productivity ......... 24

3.3. Comparing of differences between farms of different profitability levels

with similar structural characteristics .................................................................... 34

3.4. Summary and discussion of the results ................................................................ 38

4. Concluding remarks ............................................................................................................. 41

References .................................................................................................................... 42

Appendix A. Summary statistics of variables for DEA model, 2008-2013 ......................... 45

Appendix B. Summary statistics of variables for DEA model for individual years,

2008-2013 ................................................................................................................................... 47

Appendix C. Total factor productivity change, 2008-2013 .................................................. 50

4

List of abbreviations

AP – animal production

BP – balanced panel

CP – crop production

CZ – climatic zone

Coef. – coefficient

DEA – data envelopment analysis

Ha- hectares

Mln – million

TE – technical efficiency

TFP – total factor productivity

TP – total production

Tsd – thousands

UAH – Ukrainian hryvnya

UP – unbalanced panel

5

1. Introduction

1.1. Motivation and statement of the research question

Because of large areas with favorable soil qualities, Ukraine features some of the prime

locations for crop production in the world. Agricultural production from Ukrainian farms

thus can play an important role to provide food for an increasing world population and,

on the other hand, can represent an important income source for Ukrainian rural

population. In fact, agriculture accounts for a significant share in gross domestic

product and export revenues. For instance, Ukraine is the fourth largest exporter of

maize worldwide in 2013 (by holding this rank since 2005) and the tenth largest

producer of wheat over the years 2011 to 2014 (FAOSTAT, 2016).

While crop production worldwide increases steadily over the last decades, Ukrainian

agriculture (as other former socialist countries) had to sustain a harsh drop in absolute

and relative production after the collapse of the Soviet Union. Only recently, the

production value of crop production (in constant prices of 2010) recovered and

exceeded the level of 1991. For most crop products, for instance wheat and maize, the

worldwide trend of increasing production is simultaneously accompanied by a reduction

(wheat) or under-proportional expansion (maize) of the harvested area (FAOSTAT,

2016), which highlights a significant increase in productivity, e.g., in terms of yields per

hectare (see Figure 1). We also obtain this trend for Ukraine, in the case of maize even

more pronounced. Despite the presumed favorable natural conditions, however,

Ukrainian average yields only recently exceeded world averages. The question therefore

arises whether Ukrainian farms exploit the yield potential of their land or if there is a

considerable yield gap that, given appropriate management, can cause significant

production growth.

In fact, the discussion of yield gaps has attracted a lot of attention over the past two

and a half decades. Per definition, yield gaps are differences between yield potential

and the average farmers’ yields for a given region and growing season, where the yield

potential refers to the yield a crop would reach under optimal conditions, i.e., without

water, nutrients, pests, or diseases stress (Lobell et al., 2009). Commonly this yield gap

is larger for developing and transition countries than for developed countries (Neumann

et al., 2010). The yield gap is smaller if the production system relies on irrigation rather

than on (uncertain and uncontrollable) rain-fed water supply (Schierhorn, et al. 2014).

These aspects are relevant for Ukraine as well as Central Eurasia - a region that

reportedly has the largest yield gaps worldwide (Neumann et al., 2010). The definition

of the yield gap, however, focuses on the natural conditions of crop production but

often or mostly ignore the question whether the maximum possible is also economically

optimal.

6

Often the argument is made that given the existence of the (seemingly considerable)

yield gap, Ukrainian farms might be able to substantially increase total crop production.

Whether this claim is justified depends however, e.g., on the economic and political

environment of Ukrainian farmers. This paper will shed some light into these issues by

investigating the productivity and efficiency of Ukrainian farms and their determinants.

Moreover, the question is tackled whether farms have a rational choice to improve

productivity given adverse political and economic conditions.

Figure 1. Yield development of major crops in Ukraine (UA) and Worldwide (W), t/ha

Source: Own calculation based on data from FAOSTAT (2016

1.2. Structure and development of crop production in Ukraine

The agricultural sector plays an important role for the Ukrainian economy. In 2013,

agriculture accounts for 8.8% of gross domestic product (GDP), 26.9% of export

revenues (excluding revenues from services), and employs 8.1% of the working

population (SSSU, 2014a; SSSU, 2014b). Besides the ongoing geopolitical frictions, the

current situation of the agricultural sector, however, is still affected by the transition

from a planned to a free market economy. Following the collapse of the Soviet Union

and Ukraine’s independence, Ukrainian farms were confronted with new and difficult

internal and external conditions including an underdeveloped banking system, the

strong reduction of subsidies paid by the government, the disconnection of former

supply chains, and the need to integrate into global markets. The farms themselves

were mostly collective enterprises with high debts and no or vague ownership of

production factors. Under these circumstances, agricultural production value

dramatically declined by 64% from 1991 to 1999. Only after new regulations for

0

1

2

3

4

5

6

7

1991 1993 1995 1997 1999 2001 2003 2005 2007 2009 2011 2013 2015

Wheat (UA) Wheat (W) Maize (UA)

Maize (W) Sunflower (UA) Sunflower (W)

7

agricultural taxation were introduced in 1999, and the improved access to capital and

infrastructure together with trade liberalizations and the farms organizational

transformation took effect (Osborne and Trueblood, 2002; Matyukha et al., 2015), the

negative trend has been reversed. However, the total agricultural production value in

2014 was still lower relative to 1991, which is mainly caused by the low level of

production value from animal production (see Figure 2). While crop production exceeds

the 1991 level in 2013 and 2014, animal production barely reaches the level of 50% of

the base year (1991) in 2014.

Figure 2. Agricultural production value, 1991 = 100%

(calculated based on constant prices of 2010)

Source: SSSU, 2016

The relative better performance of crop production underlines the favorable conditions

for this farming orientation and we are going to detail some major factors and

developments in the following.

According to official statistics, 41.5 mln hectares or approximately 71% of the total area

in Ukraine are agricultural land. Individuals or households use one third of this land

while about half of the agricultural land is operated by commercially oriented farms,

namely 20.4 mln hectares (SSSU, 2014a). The average farm size was about 2120 ha in

2013. The farm size distribution is shown in Figure 3. The comparison of the two

pictured years (2008 and 2013) indicates a slight increase in total land use by small

farms with less than 100 ha while all other groups decline in total land use. This decline

is caused by the rapid increase of land farmed by agroholdings as illustrated on the

right hand side of Figure 3. With 5731 ha per holding subsidiary, these organizations

are significantly larger than the average farm.

0%

20%

40%

60%

80%

100%

120%

19

91

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92

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14

Total production Crop production Animal production

8

Figure 3. Distribution of farms by the sizes of land area in use

Source: AgriSurvey, 2013

Because the total number of farms in Ukraine is decreasing, the average farm size

increases overall. However, the decline in farm numbers differs across farm types (see

Figure 4). For instance, the number of private enterprises declined by 8%, peasant

farms1 by 9%, agricultural cooperatives by 51%, and other types of enterprises by

55%, while the number of business partnerships slightly increased (+3%).

Figure 4. Number of active farms in Ukrainian agriculture

Source: SSSU, multiple years

1 This farm group mainly represents small market players with an average land area of about 100 ha but there are also large peasant farms that operate more than 20 tsd ha. In general, farms in this group

feature a dynamic increase in farm size from 92 ha in 2006 to 119 ha in 2014 (i.e., an increase of almost 30%).

0%

5%

10%

15%

20%

25%

30%

up to 100 ha 101-500 501-1000 1001-2500 2501-5000 5001-10000 10001+ Holdings

2008 2013

37000

38000

39000

40000

41000

42000

43000

44000

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

2006 2007 2008 2009 2010 2011 2012 2013 2014

Peasant farms

State farms

Business partnerships

Private enterprises

Agricultural cooperatives

Other types

Peasant farms (right axis)

9

Driven by the export oriented production – more than half of the produced cereals were

exported in 2013/14 – the crop mix considerably changed over the last two decades. In

2013, the six major crops (wheat, barley, corn, sunflower, soya, and rapeseed)

accounted for 92% of the sowing area and 87% of revenues (see Figure 5 and 6). It is

noteworthy that some of these “cash crops” were only marginally important in 1990.

For instance, the share of corn increased from 3% to 20% of the total sowing area

while sunflower (from 5% to 22%), soya (from 0.3% to 9%), or rapeseed (from 0.3%

to 5%) show similar developments (see Figure 5). These crops have substituted fodder

and some niche crops, which were intended mainly for the domestic market.

Figure 5. Structure of sowing areas


* without Crimea and occupied territories of Donetsk and Lugansk regions

Accordingly, the sales structure (see Figure 6) also features an increase in the share of

corn (from 12% in 2008 to 28% in 2013), sunflower (from 12% in 2008 to 19% in

2013) and soya (from 3% in 2008 to 7% in 2013).

0%

20%

40%

60%

80%

100%

1990 1995 2000 2005 2010 2011 2012 2013 2014*

Wheat Barley

Corn Other grains

Sunflower Soya

Rapeseed Sugar beets

Corn for silage and green fodder Other crops

10

Figure 6. Revenues from the sales of crops by farms of Ukraine, mln UAH

(in constant prices of 2008)

Source: own calculations based on SSSU (multiple years)

As initially illustrated, there is an increase in factor productivity of the input land (see

Figure 1), but also a considerable rise in labor productivity from 2000 to 2014 (about 8

times) as shown by Figure 7. Structural changes (e.g., the decline in animal production,

technological change) caused here a reduction of workforce by 79% (2014) compared

to the base year (2000).

Figure 7. Number of employees and labor productivity, 2000 = 100%


83%

84%

85%

86%

87%

88%

0

10000

20000

30000

40000

50000

60000

2008 2009 2010 2011 2012 2013 Sh

are

of

"ca

sh

" c

rop

s,

%

Re

ve

nu

es,

mln

UA

H

Wheat Barley Corn

Sunflower Soya Rapeseed

Others Share of "cash" crops

0%

100%

200%

300%

400%

500%

600%

700%

800%

900%

0%

20%

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2000 2005 2010 2011 2012 2013 2014

Lab

or

pro

du

ctiv

ity

Nu

mb

er

of

em

plo

yee

s

Numer of employees Labor productivity

11

Relating to the cost level (Figure 8), we see that labor was substituted by capital

causing an intensification, which bolstered the yield development. In the period 2008-

2011 farms spent about two thousand UAH per hectare and year in average (inflation

adjusted), while the costs per hectare rose to 2.6 thousand UAH in 2012-2013. The

major components of this intensification were technological improvements (e.g., better

machinery and techniques reflected in the doubling of depreciation costs) and some

material inputs (see Table 1). The main source for increasing capital input were

comparable high profits in the years 2008-2011, but price developments in world

markets caused lower profit margins in 2012-2013, which sparks concerns regarding

the continuation of this trend.2 However, Ukrainian farms produced a record 62.2 mln

tons of cereals in 2013.

Figure 8. Total costs and revenues per hectare in crop production by farms,

(inflation adjusted)

Source: own calculations

Table 1. Production costs in CP (deflated), UAH/ha

2008 2009 2010 2011 2012 2013

Total costs 2077 1962 2027 2137 2577 2590

Labor 213 192 185 166 157 148 Social costs 51 58 66 54 83 55

Mate

rial co

sts

total 1410 1239 1278 1427 1763 1823

seeds 257 239 216 222 285 310 other agricultural products 13 13 12 13 31 17 fertilizers 366 295 296 330 359 393 oil products 313 237 253 291 409 400 electricity 21 23 25 24 24 26 fuels 20 18 16 22 36 38 spare parts 121 126 139 160 185 175

2 According to FAO, cereals price index dropped by 4.9 points in 2012 and by 16.8 points in 2013, while

for oils by 30.6 and 30.9 points respectively.

0

500

1000

1500

2000

2500

3000

2008 2009 2010 2011 2012 2013Production costs Revenue

12

3rd party services 298 289 322 366 433 466

Depreciations 117 191 194 197 240 224 O

ther

cost

s total 286 281 303 293 334 339

land rent 174 175 175 155 152 151 property rent 3 2 2 2 2 2 other 110 105 126 137 180 186

Source: own performance

As mentioned earlier, one emerging phenomena over the last two decades in Ukrainian

agriculture is the increasing importance of agroholdings, which can be defined as large

vertically and/or horizontally integrated farm enterprises. After the turn of the

millennium these organizations developed rapidly. Today, agroholdings manage almost

30% of the land in Ukraine (Figure 9) and they account for a large share of agricultural

production. In 2014, 20% of all crop products (19% in 2013) were grown or processed

by agroholdings. Relative to the average non-holding farm size in Ukraine with 1682 ha,

agroholdings are significantly larger in terms of land endowment (5731 ha per farm or

holding). Because of this size, these farms might be able to utilize economies of scale

and size and be better equipped for investments in modern technologies and

infrastructure.

Figure 9. Land bank of agroholdings in Ukraine

Source: The Largest Agroholdings of Ukraine 2015, AgriSurvey

1.702.73 3.09

4.005.10

5.60 6.04 5.85 5.60

0

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10

15

20

25

30

2007 2008 2009 2010 2011 2012 2013 2014 2015

0

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landbank of agroholdings, mln.ha % of land in use of agricultural enterprises

mln. ha %

13

Crop production

Households Independent farms Agroholdings

Figure 10. Share of agroholdings in crop production in 2014

Source: AgriSurvey, 2015

19.6%

39.7%

40.6%

14

2. Data and methods

To investigate whether and to what extent a potential yield gap is rooted in farm-intern

economic conditions, we conduct an efficiency analysis and regress upon the

determinants of the farms’ efficiency or inefficiency. We also approach the question if

efficiency is always accompanied with or a precondition for high profitability. The

rationale behind this procedure is as follows: if Ukrainian farms are highly efficient,

there is less room for inter-farm improvements and the perceived yield gap is the

difference between the fictive optimal yield and the actual yield of best practice. If

inefficiencies are observed, however, their sources might help to understand what hold

farms back from achieving higher productivity. Subsequently, we present the dataset

and briefly describe the methods used.

In general, this analysis follows the study by Balmann et al. (2013) but we use an

extended dataset, differentiate for production regions (climatic zones), and apply

additional methods (e.g., truncated regression and treatment effect analysis as

described below). Most of the results by Balmann et al. (2013) are supported by our

findings while some new insights and/or more detailed observations are presented in

this paper.

2.1. Dataset

Our study is based on farm-level accounting data of Ukrainian farms provided by the

State Statistics Service of Ukraine (SSSU). These data cover the time period 2008 to

2013. The original dataset consists of 51 686 observations of Ukrainian farms with crop

production and of various legal forms and sizes.

In order to control for the price development effect, the data have been deflated using

input and output price indices on disaggregated level. To eliminate inconsistent entries

and outliers as well as to prepare the data for the efficiency analysis, a two-stage data

cleaning was conducted. In the first stage three standard deviations threshold

procedure and histogram analysis were used (see Table 2 for the estimated upper limit

of ratio indicators), while the second stage identified and removed observations with

super efficiency values above 150%. As a result of the data cleaning, 6 785

observations (13%) were excluded and the obtained dataset contained 44 901

observations for a five-year (2008 to 2013) unbalanced panel and 26 982 observations

for a balanced panel (same time period). The share of agroholdings in the total number

of farms is 9(8) % in the unbalanced (balanced) panel3. Compared to the data of

statistical yearbooks, provided by the SSSU website, the unbalanced panel covers

between 85 to 91% of arable land, employees, revenue, or costs in crop production

3 The share of holdings in the original dataset also amounts to 9% of all farms.

15

through the observation period. This illustrates the representativeness of the dataset for

Ukrainian crop farms.

Table 2. Cleaning parameters for ratio indicators

Ratio 2008 2009 2010 2011 2012 2013

(Material costs + depreciation) / CP value4

<2.1 <1.8 <1.9 <1.8 <2 <1.9

Arable land / CP value <3.1 <3 <2.8 <2.1 <2.1 <1.5 Labor in CP / CP value <0.08 <0.07 <0.07 <0.06 <0.045 <0.03 CP value / (material costs + depreciation)

<5.8 <5.5 <5.5 <5.5 <4.5 <4.5

CP value / arable land <9 <9 <9 <10 <10 <11 CP value / labor in CP <830 <900 <900 <1150 <1250 <1400

Note: lower limit is always greater than 0, while upper limit is provided in the table.

Monetary values are inflation adjusted.

Source: own performance

To track peculiarities of production technologies caused by natural conditions, we

distinguish three climatic zones (production regions), which are composed of six

agroclimatic zones in Ukraine (Bulava, 2008), including two mountain regions (Crimea

and Carpathian). We merge the Carpathian region with the 1st climatic zone (1st climatic

zone: moist, moderately warm); also, two southern agroclimatic zones (2nd climatic

zone: representing “dry and very warm” and “very dry and very warm” conditions) are

subsumed. The 3rd climatic zone consists of Crimea mountainous region and the South-

Eastern regions. Figure 11 shows the location of the three zones as used in this study.

4 Note that, e.g., the share of material and capital costs in crop production value should be less than 1 for

positive profits, to be efficient, and to be productive. However, in our analysis we leave some space for

inefficient farms (eliminating only extreme or zero values) in order to obtain a comprehensive picture of Ukrainian agriculture.

16

1st climatic zone – enough moisture, moderately warm; 2nd climatic zone – not enough moisture, warm; 3rd climatic zone – (very) dry, very warm.

Figure 11. The three Ukraine climatic zones (production regions).

Source: own presentation based on Bulava (2008)

2.2. Data envelopment analysis (DEA)

In order to analyze recent productivity developments of farms, we investigate technical

efficiency and total factor productivity based on a standard Data Envelopment Analysis

(DEA) framework. This method makes use of linear programming to construct a non-

parametric piecewise surface (frontier) over the data which allows deriving efficiency

scores relative to this frontier (Coelli et al., 2005). The model is specified as a single

output - multiple input problem, and assumes output-oriented optimization with

constant returns to scale. The analysis is carried out on the balanced as well as the

unbalanced data panel for 2008-2013 with respect to individual frontiers for each year.

One disadvantage of the method is its limitation in controlling for data (measurement)

errors and effects of differences in production conditions (e.g., land quality). Therefore,

the rigorous data cleaning was carried out before the data were processed in the DEA.

Additionally, we differentiate between three production zones with different climatic

conditions.

The DEA model includes one output and three input variables. Appendix A provides

main statistics for the listed DEA variables for the years 2008-2013 and for the group of

observations in the balanced and unbalanced data panel with respect to the

corresponding climatic zones. Descriptive statistics for individual years are presented in

Appendix B. Output is represented by crop production value, which is derived from

production sales. This value thus considers real sales prices in aggregating the farms’

production volumes. Such a measure has the benefit of reflecting possible differences in

product quality along the technical ability to produce. Among the three input variables,

two are expressed in natural values (quantities) - land in hectares and labor in

17

employees in the given year (full-time employee equivalent). The third input variable is

the sum of material costs and depreciations as an indicator of capital costs. These two

input categories are pooled because a significant share of material cost refers to

services provided by third parties. Such services do not entail purely provision of

materials, but also their application that reduces the need for own machinery,

equipment or other capital.

2.3. Regression analysis

The technical efficiency scores derived from DEA as well as crop yields (wheat, corn and

sunflower) are further regressed upon a number of variables that may contribute to the

explanation of these variables’ variation among the farms. Accordingly, model

specifications detailed in Table 3 were used. For the yield regression, we used simple

ordinary least squares (OLS) regression and to estimate the technical efficiency scores a

truncated regression model was used because the dependent variable is restricted

between 0 and 1 here.

The explanatory variables take account of several structural farm characteristics such as

size, specialization, input use intensity, etc. The regression analysis considers also

control variables (time, holding membership dummies). Technical efficiency scores are

measured with respect to the individual years’ frontiers and with regard to climatic

zones. Additional variables account for size effects in groups of “smaller” and “larger”

producers. A farm size dummy [arable land > median (dummy, by CZ)] represents

farms larger than median in terms of total arable land while a crop specific harvested

area dummy [crop harvested area > median (dummy, by CZ)] relates to farms with

larger harvested area of the analyzed crop. Because we assume a positive correlation of

knowledge and year-to-year production, we test whether continuous production of a

particular crop [experienced crop producer (dummy)] realize higher yields. The last two

variables, profit per hectare in crop production [profit in CP (1 year lag)] and VAT

reimbursements in crop production [VAT support in CP (1 year lag)], allows to track for

the influence of earnings and state support in previous year on efficiency and

productivity of the analyzed year. These estimations are based on the balanced panel

data for 2008-2013.

18

Table 3. Description of explanatory variables in regression analyses of

technical efficiency, crop yield and crop production value determinants

Variable Description Model spe-cifications*

yield Yield wheat/corn/sunflower (t/ha) DV

te (bp) Technical efficiency (balanced panel) DV

harvestedarea Harvested area of wheat/corn/sunflower (ha) +

arableland Arable land (ha) +

landrent_ha Land rent per ha (tsd UAH/ha) + +

laborcosts_ha Labor costs per ha (tsd UAH/ha) + +

seeds _ha Costs of seeds per ha (tsd UAH/ha) + +

fertilizers_ ha Fertilizers costs per ha (tsd UAH/ha) + +

3rdparty_othercosts _ha

3rd party services and other material costs per ha (tsd UAH/ha)

+

otheragproducts_ha Costs of other agricultural products used in production, per ha (tsd UAH/ha)

+

oils_ ha Costs of oils per ha (tsd UAH/ha) +

electricity_ ha Electricity costs per ha (tsd UAH/ha) +

fuels_ ha Fuel costs per ha (tsd UAH/ha) +

spareparts_ ha Costs of spare parts per ha (tsd UAH/ha) +

propertyrent _ha Costs of property rent per ha (tsd UAH/ha) +

3rdpartyservices_ha 3rd party services per ha (tsd UAH/ha) +

other_matcosts_ha Other material costs per ha (tsd UAH/ha) +

other_procosts_ha Other production costs per ha (tsd UAH/ha) +

depreciation_ha Depreciations per ha (tsd UAH/ha) + +

cropshare_arableland Share of wheat/corn/sunflower area in total arable land

+

animprod_dv Animal production dummy, if farm has animal production = 1, otherwise 0

+ +

shareap_totvalue Share of animal production in total production + +

othercpvalue_cpvalue Share of niche crops in crop production (“cash” crops: wheat, barley, corn, sunflower, soybean, rapeseed; others – niche crops)

+ +

td2009 Time dummy - 2009, year 2009 = 1, otherwise 0

+ +


+ +


+ +


+ +


+ +

holding_dummy Agroholding farm dummy, agroholding farms = + +

19

Variable Description Model spe-cifications*

1, independent farms = 0 arable land > median (dummy, by CZ)

Farm size dummy, for farms > median = 1, otherwise 0. The median values were calculated for each model separately (i.e., it depends on crop and climatic zone)

+ +

crop harvested area > median (dummy, by CZ)

Crop (wheat/corn/sunflower) harvested area dummy, for farms > median = 1, otherwise 0. The median values were calculated for each model separately (i.e., it depends on crop and climatic zone)

+

experienced crop producer (dummy)

Dummy represents farms that had produced particular crop (wheat/corn/sunflower) during the whole period of analysis: if production > 0 each year during 2008-2013 = 1, otherwise 0.

+

profit in CP (1 year lag)

Profit per hectare in crop production with 1 year lag (tsd UAH/ha)

+ +

VAT support in CP (1 year lag)

VAT reimbursement in crop production per ha with 1 year lag (tsd UAH/ha)

+ +

* DV – dependent variable, “+” – independent variable,

Note: The yield model specification is crop specific (for wheat, corn, and sunflower). Each

specification consists of variables related to the production costs of the respective crop (e.g.,

wheat yield is explained by wheat production costs), while for crop production value and

technical efficiency models, total crop production costs are used.

2.4. Treatment effect analysis

In order to explain the differences between more and less profitable crop producing

farms, we employ treatment effect analysis – a matching procedure that allows the

comparison of treated and non-treated groups by pairing their structural variables (see

works of Rubin, Holland, Robins, Wooldridge for more details). In our sample the

treated group is represented by farms with crop production profitability above the

median of the base year (2008) while 50 percent of farms with lower profitability belong

to non-treated group. Profitability is measured by the relation of profit to total costs.

The advantage of this method is bias elimination related to size and structural

differences of farms. As we choose a direct-covariate matching approach, an outcome is

calculated by comparing farms (“neighbors”) with similar structural characteristics.

These “neighbors” are determined with covariates’ weighted function, calculated for

each individual. The average treatment effect (ATE) is calculated as an average

difference of observed and potential outcomes of the nearest neighbors:

𝐴𝑇𝐸 = 𝐸(𝑦1 − 𝑦0) .

20

One requirement for reliable results under the application of this method is a large

sample size, which our dataset provides5.

3. Results

3.1. Development of technical efficiency and total factor productivity

Our results show surprisingly low technical efficiency among Ukrainian farms. This not

only indicates highly heterogeneous farm performances but also a significant potential

of improvement on the farm level. In the sample, up to 30% of the farms are

unprofitable (in each year), which provides one explanation for such low technical

efficiency and indicates a high correlation between technical efficiency and profitability.

Estimations for both the unbalanced (see Table 4) and balanced panel (see Table 5)

show similar trends: an increasing mean of the efficiency scores for the 1st and 2nd

climatic zones, while there is no or even a negative change of this indicator in the 3rd

region. Overall, efficiency is slightly higher for the balanced panel, which highlights the

effects of inefficient farms that leave the sector at some point (in the unbalanced panel)

or that new (or merged) farms run through an adjustment period and might require

some time to improve their performance. But even for the balanced panel and best year

(2012), on average farms in the 1st climatic zone could (theoretically) increase output

levels by 43% without changing the level of input.

Table 4. Technical efficiency development (unbalanced panel), 2008-2013

2008 2009 2010 2011 2012 2013

Climatic zone 1

Number of observations

1462 1347 1156 1196 1119 1090

Mean 0.367 0.412 0.407 0.393 0.482 0.429 Standard deviation 0.169 0.163 0.168 0.165 0.188 0.173

Climatic zone 2


2314 2248 2170 2166 2185 2193


Climatic zone 3


3883 3970 4115 4247 4022 4018


Note: means are geometric Source: own calculations 5 Estimations were done using “teffects nnmatch” command in Stata software (StataCorp., 2015).

21

Table 5. Technical efficiency development (balanced panel), 2008-2013

2008 2009 2010 2011 2012 2013

Climatic zone 1


560 560 560 560 560 560


Climatic zone 2


1325 1325 1325 1325 1325 1325


Climatic zone 3


2612 2612 2612 2612 2612 2612


Note: means are geometric


The development of total factor productivity (TFP), efficiency and technological change

is summarized in Figure 12 (further results including the decomposition of the efficiency

change are presented in Appendix C). Low efficiency scores might be due to high

technological change. In fact, we observe a positive development in climatic zones 2

and 3, while a negative trend is obtained in region 1. Besides that these dynamics seem

insufficient to explain the low technical efficiency, there is also considerable variation

over the observed years. Except for climatic zone 1, the annual average efficiency

change is below one, suggesting that farms become more heterogeneous in efficiency

(i.e., the distance between best and worst farms increases). The combination of both,

technological and efficiency change, provides the measure for total factor productivity

(TFP). While farms in climatic zones 1 and 3 became more productive, the opposite is

true for farms in climatic zone 2 (based on the annual average).

Of course, one of the main drivers behind the observed dynamics relates to weather

conditions. For instance, the technical efficiency drop in climatic zone 1 in the years

2010 and 2012 are largely caused by adverse weather conditions (cf. Figure 1,

representing crop yields). Other impacts concern the political framework as illustrated

by the negative TFP change in climatic zone 2. This region is the major grain producer

and therefore it was over-proportionally affected by the introduction of trade

restrictions (export quotas in 2010 and export duties in 2011), which let prices

increasingly diverge in Ukrainian and foreign markets (Kulyk et al., 2014). Farms in

22

climatic zone 3 show positive TFP change due to positive technical change while

efficiency change was slightly negative. The latter is caused by the decrease of crop

yield accompanied by increasing costs in 2009 and increasing heterogeneity across

farms (decreasing scale efficiency) over the whole time period. Pure efficiency change is

slightly positive over the considered years though. These developments are summarized

in Table C of the Appendix and illustrated in Figure 12.

Climatic zone 1

Climatic zone 2

0.700

0.800

0.900

1.000

1.100

1.200

1.300

1.400

2008~2009 2009~2010 2010~2011 2011~2012 2012~2013

TFP change Technology change Efficiency change

0.800

0.850

0.900

0.950

1.000

1.050

1.100

1.150

2008~2009 2009~2010 2010~2011 2011~2012 2012~2013

23

Climatic zone 3

Figure 12. Average annual technical efficiency, technical and total factor

productivity changes, balanced panel, 2008-2013


0.800

0.850

0.900

0.950

1.000

1.050

1.100

1.150

1.200

1.250

2008~2009 2009~2010 2010~2011 2011~2012 2012~2013

24

3.2. Identification of important determinants of efficiency and productivity

While the previous section shows the efficiency and productivity developments, we now

proceed with the determinants behind these measures in greater detail. First, we

analyze crop yield determinants (as one of the most important productivity indicators)

before we account for efficiency determining factors directly.

Estimation of crop yield determinants

As crop yields are an important productivity indicator, it is crucial to define the pivotal

factors affecting yield and if they differ among the regions (climatic zones) and crops.

In this section, we analyze three major crops: wheat, corn, and sunflower. Figure 13

shows the shares of these crops in sowing areas across the three zones (in 2013). We

note the dominant role of wheat and sunflower for climatic zone 1, while corn is the

major crop in the central and northern parts of Ukraine (climatic zones 1 and 2). More

specifically, 24% of the area is covered by wheat and 25% by corn in climatic zone 1

while climatic zone 2 features 33% corn, 32% wheat and 28% sunflower, respectively.

The yields of these crops naturally vary across climatic zones. The highest yield level is

achieved in climatic zone 2 due to favorable temperatures, humidity, and soil qualities.

In the case of corn, however, high yields are observed in climatic zone 1 as well.

Comparing the costs and revenues in this figure, we can observe some degree of

correlation. However, due to price decreases on key commodities both on the world and

the domestic markets, in average only for climatic zone 2 revenues exceed costs in case

of grains in 2013. Average profitability figures6 on the country-level, published by State

Statistic Service of Ukraine (2014c), also indicate low financial results of grain

production (2.4% for wheat and 1.5% for corn) and better results for sunflower

(28.5%).

6 As a ratio of profit to costs

25

Wheat

Corn

Sunflower

Figure 13. Distribution of sowing areas, production costs, revenues and

yields in Ukraine in 2013

Source: SSSU (2013), own representation.

3.24

4.44

3.71

3.51

5.03

4.53

3.64

4.90

4.99

0 2 4 6

CZ3

CZ2

CZ1

Costs, tsd UAH/haRevenue, tsd UAH/ha

5.48

7.55

7.12

5.04

7.42

7.15

5.28

6.97

7.19

0 2 4 6 8

CZ3

CZ2

CZ1

2.28

2.76

2.23

5.90

6.77

4.80

4.33

5.49

4.95

0 2 4 6 8

CZ3

CZ2

CZ1

26

Note: The maps represent the shares of sowing areas covered by the specific crop in

each region. The climatic zones are highlighted with a black border. The charts

represent mean values of costs, revenues and yields.

Wheat yield determinants. As Table 7 shows, fertilizer [fertilizers] is the most

limiting production factor for wheat yield. According to our model, the additional

application of fertilizer (in terms of higher input costs) of 1 thousand UAH per hectare

would cause an increase in yield by about 1 to 1.2 tons per hectare. Contrarily, seed

costs [seeds] have no significant influence on wheat yield except of climatic zone 2.

However, even in this region the effect is comparatively small. This might highlight that

most farms use seed from the last season’s harvest. Services of third-party

organizations [3rd party services] and other material costs [other material costs] also

have a significant positive impact on wheat yield. Unfortunately, we cannot decompose

these groups further, but we should note that both may include crop protection costs

(either as a service of application or as a purchase of crop protection products), while

machinery leasing and direct production and harvesting services could be also included

in third party services.

Other groups of production costs such as land rent payments [land rent per ha], labor

[labor costs in wheat production], depreciation [depreciation], and other production

costs [other production costs], have statistically significant, positive influence on wheat

yield. Concerning the labor costs in crop production, we might note that there can be a

bias as farm managers may not track for the actual time allocated to a single crop.

Thus, they – to avoid additional transaction costs – may distribute the labor costs of a

farm arbitrarily among crops. As long as this distribution is consistent over time, the

bias might be small but, nonetheless, we cannot differentiate heterogeneous labor costs

developments among the different crops. In the case of land rent payments, our results

indicate that yield is sensitive regarding land quality, where we observe higher

dependence of yield on land quality (approximated by rental payments) in regions with

less favorable soils conditions (climatic zone 1) as well as with low precipitation and

high temperatures (climatic zone 3).

The share of wheat in total sowing areas of a farm [share of wheat in sowing area] in

climatic zones 1 and 3 show statistically significant negative effect on yield. This may

suggest that the dominant role of these crops within the crop rotation is sufficiently

high to depress yields, e.g. by soil depletion or the occurrence of crop-specific weeds,

insects, and fungus. However, this effect can also relate to intra-regional differences in

soil quality and/or climatic conditions for which we cannot control. In particular, this

seems to be plausible for climatic zone 1 where a broader crop rotation (that also

includes niche crops [share of niche crops in CP]), provides a negative yield effect;

something we also observe in region 2. A positive yield effect is obtained for farms with

27

animal production [animal production (dummy)] in climatic zones 2 and 3. This is in line

with the previous finding such that farms can, e.g., use the manure to improve the

fertilizer application practices in wheat production. However, the effect of the share of

animal production [share of AP in TP] has no statistically significant influence on yields.

We further obtain some evidence of scale effects. In terms of arable land, larger farms

[arable land > median (dummy, by CZ)] achieve higher wheat yields in climatic zone 2.

The harvested area of wheat, included into the model as a continuous variable, shows a

statistically significant positive effect on a low level in climatic zone 1. However, larger

wheat producers – in terms of harvested area separated by median level [wheat

harvested area > median (dummy, by CZ)] – achieve higher yields across all climatic

zones. The holding affiliation [holding dummy] provides a positive influence only in

climatic zone 3, a region with the smallest share of land harvested by holdings

(approximately 17% compared to 28% in climatic zone 1 and 37% in climatic zone 2).

Moreover, experienced wheat producers [experienced wheat producer (dummy)], who

harvest wheat in all considered years (i.e., from 2008 to 2013) also achieve higher

yields in climatic zones 1 and 2. This might indicate learning effects, while the negative

effect in climatic zone 3 may suggest that experienced farmers apply cost minimizing

production practices to manage climate risks. The data provide some support for this

account: In wheat production and compared to climatic zone 1, farmers in climatic zone

3 had 17% lower production costs per hectare, whereas the yield difference was only

5% (see Figure 13).

Our results also suggest that higher profit per hectare received in the previous year

[profit in CP (1 year lag)] influences wheat yields positively. These effects are

consistent across all climatic zones. We also observe the positive effect of VAT

reimbursement [VAT support in CP (1 year lag)] on wheat yield in climatic zones 1 and

2, while in climatic zone 3 it is negative.

Table 6. Parameter estimates of regression model of wheat yield

determinants, balanced panel, 2008-2013

Climatic zone 1 Climatic zone 2 Climatic zone 3

Coef. P>|t| Coef. P>|t| Coef. P>|t|

harvested area (wheat) 0.000 0.021 0.000 0.806 0.000 0.877

land rent per ha 0.340 0.000 0.161 0.007 0.392 0.000

labor costs in wheat production

1.224 0.000 0.866 0.000 1.069 0.000

seeds 0.092 0.391 0.184 0.011 -0.060 0.301

fertilizers 1.192 0.000 1.070 0.000 1.020 0.000

3rd party services 0.755 0.000 0.613 0.000 0.880 0.000

28



other material costs 0.679 0.000 0.591 0.000 0.797 0.000

depreciation 0.656 0.000 0.571 0.000 0.570 0.000

other production costs 0.255 0.000 0.490 0.000 0.493 0.000

share of wheat in sowing area

-0.802 0.000 0.053 0.683 -0.251 0.001

animal production (dummy)

0.054 0.198 0.199 0.000 0.167 0.000

share of AP in TP -0.062 0.461 -0.104 0.148 -0.051 0.416

share of niche crops in CP -0.400 0.000 -0.640 0.000 0.052 0.408

holding dummy 0.001 0.986 -0.036 0.373 0.183 0.000

arable land > median (dummy, by CZ)

0.065 0.160 0.162 0.000 0.026 0.219

wheat harvested area > median (dummy, by CZ)

0.200 0.000 0.088 0.013 0.145 0.000

experienced wheat producer (dummy)

0.194 0.005 0.136 0.002 -0.097 0.000

profit in CP (1 year lag) 0.246 0.000 0.255 0.000 0.224 0.000

VAT support in CP (1 year lag)

0.267 0.041 0.222 0.000 -0.221 0.000

Constant 0.840 0.000 1.435 0.000 1.565 0.000

Nr. of observations 2628

6026

12212

Prob > F 0.000

0.000

0.000

R-squared 0.654

0.547

0.498

Adjusted R-squared 0.651

0.545

0.497


Note: To keep the table at reasonable length, the time dummies considered in the

model are not reported. Arable land median: CZ1 – 1193 ha, CZ2 – 1623 ha, CZ3 –

1891 ha. Wheat harvested area median: CZ1 – 348.5 ha, CZ2 – 358 ha, CZ3 – 493 ha.

All the monetary valued are deflated and calculated per hectare of harvested area.

Corn. In general, most of the findings obtained for wheat production hold true for corn

as well. For instance, farms seem to operate on low input levels making intensification

an option to considerably increase corn yield. This is implied by positive yield effects of

fertilizers [fertilizers], third party services [3rd party services], other material costs

[other material costs], as well as other production costs like labor [labor costs in corn

production] or depreciation [depreciation]. Furthermore, we also found evidence of

economies of size, i.e., larger farms [arable land > median (dummy, by CZ)] achieve

higher corn yields in climatic zones 1 and 2. However, positive scale effects with regard

to corn area ([harvested area (corn)] and [corn harvested area > median (dummy, by

CZ)]) are observed only in climatic zone 3. Thus, the expansion of corn production (in

29

terms of sowing area) in this region may contribute positively to corn yield, which

implies potential gains from specialization. This is not the case for the major production

region (climatic zone 2), which – together with a negative yield effect of an increasing

corn share of the crop area – potentially indicates that corn area is at its upper limit in

crop rotations in this region.

In contrast to wheat production, corn seeds [seeds] are crucial in providing a

(considerable) positive yield effect. This suggests that the appropriate selection of

adopted high-quality corn varieties (hybrids) substantially increase the yield level (e.g.

Troyer, 1996). Land quality (approximated by land rent [land rent per ha]) only has a

significant positive effect in climatic zone 1, while an increase of niche crops (e.g.,

indicating diversification) positively affects corn yield in climatic zone 3 (where the crop

rotation is especially narrow). Also animal production ([animal production (dummy)]

and [share AP in TP]), which might provide organic fertilizer, can contribute a positive

effect. The affiliation with a holding has no statistically significant influence on corn

productivity.

For all climatic zones, higher profitability in the previous year [profit in CP per ha (1

year lag)], and, for climatic zone 1 and 3, the permanent engagement in corn

production [experienced corn producer (dummy)] have positive influences on corn

yield. In case of VAT reimbursements [VAT support in CP per ha (1 year lag)], we

observe a positive (negative) effect in climatic zone 2 (3).

Table 7. Parameter estimates of regression model of corn yield determinants,

balanced panel, 2008-2013



harvested area (corn) 0.000 0.823 0.000 0.304 0.000 0.046

land rent per ha 0.493 0.072 0.051 0.675 -0.008 0.947

labor costs in corn production

0.838 0.000 0.501 0.000 1.130 0.000

seeds 1.233 0.000 1.016 0.000 1.337 0.000

fertilizers 1.115 0.000 1.007 0.000 1.199 0.000



depreciation 0.371 0.007 0.654 0.000 0.759 0.000


share of corn in sowing area

0.749 0.087 -1.181 0.000 1.108 0.000


-0.198 0.136 0.099 0.205 0.256 0.000

share of AP in TP 0.917 0.001 0.436 0.010 0.206 0.180

30



share of niche crops in CP -0.414 0.121 -0.717 0.000 0.366 0.053

holding dummy 0.096 0.564 -0.197 0.028 0.014 0.864


0.445 0.000 0.162 0.022 -0.143 0.002

corn harvested area > median (dummy, by CZ)

-0.304 0.019 0.075 0.347 0.321 0.000

experienced corn producer (dummy)

0.323 0.002 0.082 0.251 0.119 0.005

profit in CP (1 year lag) 0.504 0.000 0.393 0.000 0.284 0.000 VAT support in CP (1 year lag)

0.271 0.329 0.496 0.000 -0.305 0.013

Constant 1.786 0.000 3.165 0.000 1.046 0.000


4687

6752

Prob > F 0.000

0.000

0.000

R-squared 0.520

0.448

0.573


0.446

0.572



model are not reported. Arable land median: CZ1 – 1541.5 ha, CZ2 – 1822 ha, CZ3 –

2182 ha. Corn harvested area median: CZ1 – 168 ha, CZ2 – 271 ha, CZ3 – 172 ha. All

the monetary values are deflated and calculated per hectare of harvested area.

Sunflower. Compared to the previous two crops, there are no significant differences

regarding sunflower yield determinants. The major production region is climatic zone 3

and the results show that additional specialization towards sunflower [share of

sunflower in sowing area] has a negative effect in this production region as well as in

climatic zone 2. However, the yield level is positively affected almost by any

intensification measure (including fertilizer [fertilizers], labor [labor costs in sunflower

production], 3rd party services [3rd party services] or other material costs [other

material costs]). Similar to corn (but not wheat), seeds [seeds] have a positive effect.

Sunflower yield shows higher sensitivity to land quality [land rent per ha] in climatic

zone 3. Also larger farms [arable land > median (dummy, by CZ)] have higher yields in

climatic zone 3, but farms that harvest more area under sunflower [sunflower harvested

area > median (dummy, by CZ)] realize higher yields in climatic zone 2. Again we

obtain the positive relation between profitability and yield level [profit in CP per ha (1

year lag)] and the learning effect [experienced sunflower producer (dummy)] that

positively influences yield across all climatic zones. As in the case of corn, we observe a

positive effect of VAT reimbursement [VAT support in CP per ha (1 year lag)] only in

climatic zone 2.

31

Table 8. Parameter estimates of regression model of sunflower yield


Climatic zone 1

Climatic zone 2 Climatic zone 3


harvested area (sunflower)

0.000 0.526 0.000 0.800 0.000 0.014

land rent per ha 0.175 0.237 0.109 0.008 0.276 0.000

labor costs in sunflower production

0.205 0.301 0.278 0.000 0.370 0.000

seeds 0.727 0.000 0.671 0.000 0.930 0.000

fertilizers 0.274 0.001 0.465 0.000 0.508 0.000



depreciation 0.182 0.045 0.292 0.000 0.304 0.000


share of sunflower in sowing area

0.459 0.179 -0.469 0.000 -0.160 0.000


-0.104 0.107 -0.004 0.877 0.082 0.000

share of AP in TP 0.542 0.001 0.230 0.000 0.031 0.502

share of niche crops in CP -0.078 0.623 -0.380 0.000 -0.152 0.002

holding dummy -0.065 0.371 -0.045 0.161 0.035 0.157


0.061 0.333 0.033 0.163 0.035 0.015

sunflower harvested area > median (dummy, by CZ)

0.024 0.706 0.061 0.011 -0.017 0.207

experienced sunflower producer (dummy)

0.181 0.001 0.115 0.000 0.093 0.000


0.224 0.254 0.081 0.084 -0.051 0.142

Constant 0.795 0.000 1.245 0.000 0.627 0.000


4946

12185

Prob > F 0.000

0.000

0.000

R-squared 0.565

0.512

0.535


0.510

0.534



model are not reported. Arable land median: CZ1 – 1931 ha, CZ2 – 1702 ha, CZ3 –

1888.5 ha. Sunflower harvested area median: CZ1 – 120 ha, CZ2 – 220 ha, CZ3 – 450

ha. All the monetary values are deflated and calculated per hectare of harvested area.

32

Estimation of technical efficiency in crop production

The importance of determinants influencing technical efficiency of Ukrainian crop

production differs across climatic zones. For instance, in climatic zone 1, labor [labor

costs in CP], land quality [land rent], and fertilizers [fertilizers] are among the most

important drivers while in region 2 labor [labor costs in CP], seeds [seeds], and

fertilizers [fertilizers] contribute the most. For climatic zone 3, we identify land quality

[land rent], other agricultural production used as inputs [other agricultural products],

electricity [electricity], fuels [fuels], and property rent [property rent] as most

important. The crucial role of labor costs on technical efficiency can be explained from

different perspectives. For instance, this effect might be caused by the competition for

and higher costs of skilled labor (e.g., operators of new machinery) or higher salaries

may indicate a wage system that appreciates productive employees.

Diversification of production, assessed by the dummy variable “animal production

(dummy)”, shows a positive effect on technical efficiency of crop production in climatic

zones 2 and 3. However, the share of animal production in total production [share of AP

in TP] negatively influences efficiency of crop production in climatic zones 1 and 2. One

possible explanation is the use of otherwise idle capacities (e.g., buildings) for animal

production to cover some of the fixed costs. Increasing shares of animal production,

however, might cause conflicts in the use of farm resources for crop production.

Furthermore, the share of niche crops in the production structure [share of niche crops

in CP] has no statistically significant influence on technical efficiency. Regarding the size

of the farm or its integration into an agroholding, results are mostly ambiguous. Larger

farms [arable land > 1131 ha (median, dummy)] tend to have slightly higher TE in

climatic zones 1 and 3, while an agroholding-membership [holding dummy] impacts TE

negatively in the same regions.

Farms with higher profit per hectare [profit in CP per ha (1 year lag)] have usually also

higher technical efficiency and this effect is comparably high across all regions. VAT

reimbursement [VAT support in CP per ha (1 year lag)] does not show a statistically

significant effect on TE in climatic zones 1 and 2 but there is a negative effect in

climatic zone 3.

33

Table 9. Parameter estimates of regression model of technical efficiency


Climatic zone 1

Climatic zone 2 Climatic zone 3

Coef. P>|z| Coef. P>|z| Coef. P>|z|

arable land 0.000 0.007 0.000 0.000 0.000 0.161

land rent 0.097 0.000 0.061 0.000 0.108 0.000

labor costs in CP 0.114 0.000 0.071 0.000 0.027 0.012

seeds 0.034 0.033 0.065 0.000 0.044 0.000

other agricultural products

0.012 0.724 0.064 0.003 0.105 0.018

fertilizers 0.086 0.000 0.072 0.000 0.058 0.000

oils -0.087 0.000 -0.034 0.001 -0.046 0.000

electricity 0.000 0.995 -0.008 0.887 0.071 0.036

fuels 0.077 0.134 0.026 0.346 0.073 0.000

spare parts 0.016 0.305 0.027 0.001 0.014 0.086

services in CP and other material costs

0.038 0.000 0.045 0.000 0.052 0.000

property rent -0.249 0.076 -0.137 0.052 0.191 0.055

depreciation in CP 0.013 0.275 -0.001 0.865 -0.028 0.000


0.006 0.440 0.010 0.027 0.009 0.001

share of AP in TP -0.034 0.038 -0.042 0.000 0.010 0.312

share of niche crops in CP 0.010 0.445 -0.007 0.373 0.015 0.141

holding dummy -0.033 0.001 -0.004 0.462 -0.010 0.057

arable land > 1131 ha (median, dummy)

0.014 0.028 0.005 0.222 0.010 0.000


0.024 0.221 -0.008 0.341 -0.015 0.051

Constant 0.317 0.000 0.336 0.000 0.275 0.000


6329

12590

Log likelihood 1530.53

7 3928.59

0 8066.61

6

Wald chi2 (24) 1110.54

0 2044.36

0 2660.96

0

Prob > chi2 0.000

0.000

0.000


34

3.3. Comparing differences among farms of different profitability levels

with similar structural characteristics

In this section we provide results of the treatment effect analysis (TEA) where we

compare developments of TFP, TE, growth indicators, and the input structure of farms

based on their profitability in the base year (2008). Farms with a profitability level

above 13.9% (the median of all farms) are coded as “1” and belong to the treated set,

while farms with lower profitability are in the non-treated group (coded as “0”).

The treated group shows on average higher TFP change than less profitable farms with

similar structural characteristics, but their TE change is significantly lower (see Table

10). We also observed no significant differences in TFP and TE changes comparing

farms with different land endowments (based on median of 2008).

Besides the base year (2008), we conducted this analysis also for the final year of the

dataset (2013) to investigate the dynamics of the results. It showed, that farms with

higher profitability in 2008 featured higher TE scores (on average by 0.133) but due to

moderate growth rates the difference significantly decreased until 2013.

Table 10. Treatment effect analysis of TFP and TE changes in farms with high

and low profitability levels in base year, 2008-2013

Dependent variable Target

population Number of

observations Coefficient P>|z|

TFP change* All farms 4497 0.286 0.000 TE in 2008* All farms 4497 0.133 0.000 TE change* All farms 4497 -0.330 0.000 TE in 2013* All farms 4497 0.021 0.000

Source: Own calculations

* without exact matches on regions

Figure 14 also illustrates the relations between base-year TE scores and TE change over

the period of investigation. Thus, more profitable farms, which feature higher technical

efficiency, show less TE change due to decreasing marginal returns.

35

Figure 14. Correlation of TE in base year and TE change

Source: own calculation

The application of TEA to growth indicators shows that farms with above median

profitability (in 2008) have lower growth of crop production but the significantly higher

production level (on average by 1537.3 tsd UAH7) compared to farms with below

median profitability. Between the two groups, there are no significant differences in

arable land and labor, while the treated group outperforms the control group in terms

of profit per hectare and crop yield. Again, growth rates for farms of the treated group

are lower but the levels of profit and yield per ha are still higher in 2013 compared to

the non-treated group.

Table 11. Treatment effect analysis of growth indicators in farms with high

and low profitability levels in base year, 2008-2013




CP value in 2008* All farms 4497 1537.538 0.000 CP value – absolute

growth* All farms 4497 -1398.799 0.000

CP value – relative growth*

All farms 4497 -0.495 0.000

7 Median CP volume in 2008 – 3901.6 tsd UAH

01

23

45

67

TE

ch

an

ge

20

08

-20

13

(B

P)

0 .2 .4 .6 .8 1TE 2008 (BP)

TE change 2008-2013 (BP) Fitted values

36




CP value in 2013* All farms 4497 138.739 0.659

Arable land in 2008* All farms 4497 87.119 0.146 Arable land – absolute

growth* All farms 4497 -55.371 0.335

Arable land – relative growth*

All farms 4497 -0.021 0.715

Arable land in 2013* All farms 4497 31.747 0.643

Labor in CP in 2008* All farms 4497 1.089 0.491 Labor in CP – absolute

growth* All farms 4497 -0.494 0.698

Labor in CP – relative growth*

All farms 4497 0.002 0.938

Labor in CP in 2013* All farms 4497 0.594 0.665



Table 12. Treatment effect analysis of performance indicators in farms with

high and low profitability levels in base year, 2008-2013




Profit per ha in CP in 2008*

All farms 4497 0.513 0.000

Profit per ha in CP – absolute growth*

All farms 4497 -0.217 0.000

Profit per ha in CP – relative growth*

All farms 4491 -1.315 0.621

Profit per ha in CP in 2013*

All farms 4497 0.296 0.000

Crop yield in 2008* All farms 4497 0.415 0.000 Crop yield – absolute

growth* All farms 4497 -0.230 0.001

Crop yield – relative growth*

All farms 4497 -0.354 0.000

Crop yield in 2013* All farms 4497 0.185 0.006



On the input side, we see that higher profitability is correlated with lower costs. In

2008, more profitable farms had paid less for rented land, on average 23UAH/ha

(approximately 13% below the median) compared to less profitable farms.

Differentiation with respect to the investigated regions shows that there were no

significant differences in the first climatic zone, while the rental price for farms in the

37

treated group was 30(21) UAH/ha lower relative to the non-treated group in climatic

zone 2(3). Because of a less dynamic price development in the treated group, this gap

increases over the years, i.e., farms with higher profitability pay less land rent and the

price increase is smaller relative to the non-treated group. Accordingly, the price

difference in 2013 increased to 43 UAH/ha (ca. 7% of the median, see Table 13).

Material costs of the treated group were lower on average by 34 UAH (ca. 3% of the

median) per hectare in 2008 but in 2013 the difference between both groups was not

different from zero.

Farms with higher profitability seem to use superior (modern) technology (indicated by

higher capital assets), while less profitable farms rather rely on third-party services and

have a tendency to increase their use. Additionally, the treated group has higher labor

productivity (lower labor costs per hectare in CP) both in the base and the final year.

The observable increase in labor costs is higher for more profitable farms but mostly

reflects an increase of salaries. These results indicate that substitution of labor by

capital (i.e., mechanization and modernization) plays a major role in the treated relative

to the untreated group of farms.

Table 13. Treatment effect analysis of inputs in farms with high and low

profitability levels in base year, 2008-2013




Land rent per ha in 2008*

All farms 4301 -0.023 0.000

Land rent per ha – absolute growth*

All farms 4236 -0.019 0.039

Land rent per ha – relative growth*

All farms 4236 -0.411 0.162

Land rent per ha in 2013*

All farms 4336 -0.043 0.000

Material costs in CP per ha in 2008*

All farms 4497 -0.034 0.000

Material costs in CP per ha – absolute growth*

All farms 4497 0.043 0.277

Material costs in CP per ha – relative growth*

All farms 4496 0.030 0.551

Material costs in CP per ha in 2013*

All farms 4497 0.010 0.812

Material costs in CP per ha – absolute growth

Farms of the climatic zone №1

560 0.326 0.016



1325 -0.188 0.037

38






2612 0.077 0.068

Depreciation in CP per ha in 2008*

All farms 4163 0.011 0.009

Depreciation in CP per ha – absolute growth*

All farms 4085 0.040 0.000

Depreciation in CP per ha – relative growth*

All farms 4085 0.702 0.423

Depreciation in CP per ha in 2013*

All farms 4288 0.055 0.000

Third-party services in CP per ha in 2008*

All farms 4497 -0.022 0.000

Third-party services in CP per ha – absolute

growth*

All farms 4337 -0.034 0.069

Third-party services in CP per ha – relative growth*

All farms 3895 0.081 0.974

Third-party services in CP per ha in 2013*

All farms 4337 -0.047 0.005

Labor costs in CP per ha in 2008*

All farms 4497 -0.033 0.000

Labor costs in CP per ha – absolute growth*

All farms 4497 0.012 0.097

Labor costs in CP per ha – relative growth*

All farms 4492 0.092 0.469

Labor costs in CP per ha in 2013*

All farms 4497 -0.021 0.009



3.4. Summary and discussion of the results

On average, Ukrainian farms feature low technical efficiency, which highlights

considerable farm heterogeneity in terms of production performance. Based on our

differentiation of production regions, we find that water restricted regions (as climatic

zone 2 and 3) are especially prone to inefficiencies. This highlights the importance of

water in particular and weather conditions in general regarding the efficiency of farms.

While adverse weather events (as drought or very cold/long winter) might have

negative effects for all farms, local conditions and/or managerial potential play an

important role by dealing with such circumstances (Deininger et al., 2015).

39

Despite the observed high fluctuations in its development, there is a positive trend in

farm productivity, especially manifested in the gradually increasing yield levels (of major

crops). However, not all (or even not the majority of) farms can benefit from such

development which also means that a relatively small share of farms is mainly

responsible for efficiency gains. As shown by Balmann et al. (2013) or Deininger et al.

(2015), one group of farms contributing to this trend are agroholdings – seemingly

answering the call by Zynich and Odening, (2009) to vertically and horizontally integrate

to better manage credit risk. However, we could not identify a similar restriction to one

farm organization. But more broadly, other - or the majority of – farms is lagging

behind the more productive ones, which gives rise to increasing inequality in farm

performances. The relevance of this finding is underlined by the positive correlation of

efficiency and farm profitability but also by the fact that there are 15 to 30 percent of

farms annually featuring negative profits. In this respect, it is not surprising that the

developments on the world market for agricultural products have major impacts on

productivity because Ukrainian farms depend highly on cash crops that are mostly

intended for export.

Our main focus is on crop yield levels and the analysis shows that there is considerable

potential to increase crop yield by intensification. Particular importance can be

attributed to inputs as fertilizer or seed – production components that can have a

considerable impact on (cost) efficiency and therefore require appropriate technology

and managerial knowledge. Despite the already large farm structure in our sample, we

identified positive size effects while no significant effect could be found regarding the

organization of farms, i.e., whether a farm belongs to a holding or not.

Land quality has a significant effect on the productivity of farms. Given the overall low

efficiency, this might indicate that (some) production systems are poorly adopted to the

local production conditions and instead aim to maximize short term benefits by the

engagement in cash crops such as wheat, corn, and sunflower. This is also supported

by the observation that in some regions the diversification of the crop rotation positively

affects efficiency, which hints the presence of phytosanitary problems in narrow

production systems.

The major finding of the presented analysis is that almost any indicator reacts positive

towards intensification. On the one hand, farms operating on low input levels aim to

minimize costs. On the other hand, if there are positive marginal profits by

intensification, there must be reasons to not extend input use. In fact, one might argue

that most of these reasons are exogenous to farms. For instance, one source of

inefficient production is limited access to required capital, i.e., financial means for short

or long term investments. This conclusion is also supported by empirical evidence of

imperfect capital markets (Zynich and Odening, 2009).

40

A second source for (persistent) production inefficiency on the input side of the farm

might relate to land market imperfections. Not only can the scattered landownership

and large scale-farm structure give rise for local market power and restrict the access to

the major production factor (Vranken and Swinnen, 2006), but the unresolved issue to

open and liberalize the land market is a prime source for inefficient resource allocation

(Deininger et al., 2015; Lioubimtseva et al., 2013). Land is one of the most important

production factors of agriculture and if its free transfer, e.g., from inefficient to efficient

farms is restricted, inefficiencies are conserved. Our results show (at least for some

regions) positive effects of farm growth on efficiency and productivity, which requires a

sufficiently dynamic structural change.

From a farm output perspective, further reasons for inefficiencies are the presence and

increasing exposure of agriculture to different types of risk. In general, if farms are risk

averse they will reduce input levels in the face of increasing uncertainty (Chavas, 2006).

For instance, Skakun et al. (2015) show that drought risk is non-uniformly distributed in

geographical space, can be substantial, and requires for an appropriate insurance

system, which is often not available (Shynkarenko, 2007). As a consequence, farms at

different locations in a region face and are affected by risk differently without adequate

measures to manage it. Zynich and Odening (2009) also highlight the need to reduce

credit risk by appropriate risk management instruments, while farms also have to deal

with considerable and increasing market risk in terms of price volatility (Bellemare et

al., 2015). Those and similar sources of risk may cause low input levels and inefficiency

in production by Ukrainian farms.

Furthermore, the dependence to export agricultural raw products to be processed

abroad might relate to capacity constraints of domestic processing. On the one hand,

processing itself has to consider domestic demand for processed agricultural products

and if this demand is weak, the processing sector has little or no incentive to invest.

Accordingly, excess production of agricultural raw products (i.e., beyond the level of

domestic processing in downstream markets) will need to go abroad. This, however,

will transfers value added out of Ukraine. On the other hand, production for the

international market often suffers from high regulation and risk exposure for Ukraine

processors. In fact, only the sunflower processing is well developed in the sense that

more than 90% of sunflower seed are processed into sunflower oil and almost all of it is

exported each year. The contrary is the case for soya and rapeseed. In fact, there are

initiatives concerning this issue but, e g., the attempt to implement a biofuel sector

based on rapeseed to participate in this dynamic market, were unsuccessful

(Schaffartzik, 2014).

Governmental instruments as subsidies can have a positive effect on farm productivity

(Zhu and Lansink, 2010). A recent study by Curtiss et al. (2017) shows that these

41

effects vary across different farm types depending on farm size or organization, and the

time period (subsidy level). In fact, subsidies might have the potential to decrease

(increase) average farm efficiency if already efficient (inefficient) farms benefit over-

proportional from those transfers. Given the low level of government payments after

2008, such subsidy effects are rather low if not negligible. The regression in our

analysis accounts for a specific type of subsidy – VAT reimbursement. In contrast to

direct subsidies, this support mechanism became a major source of agricultural

production subsidization. Despite the absent (climatic zones 1 and 2) or negative

(climatic zone 3) effect on technical efficiency, we found a positive influence of these

payments on yields, In this case though, the impact is heterogeneous across different

climatic zones and crops. Furthermore, we need to note that most support mechanism

themselves usually induce distortions by incentivizing or preserving inefficient market

allocations of resources. The implementation of any policy instruments therefore need

to focus on the mitigation of the aforementioned market imperfections on capital and

land markets and the development of the insurance system. Appropriate measures can

help to enable or support farms to benefit from risk reducing options as insurance or

irrigation systems, which offers the potential to increase farm productivity, e.g., crop

yields.

4. Concluding remarks

In aiming to answer the question whether Ukrainian farms are able to considerably

increase total crop production, we identify substantial inefficiencies. While this might

provide indeed the potential for significant productivity improvements, some of the

necessary conditions to access this potential are exogenous to Ukrainian farms.

Whether the supposedly large yield gap can be reduced, will therefore hinge on

numerous external factors in the realm of the institutional, economical, and social

framework in Ukraine as well as the natural conditions. As always, simple or general

solutions are not available but some basic conclusions can be made. For instance,

imperfections on input and output markets restrict farm development towards more

efficient production outcomes. While there are certainly considerable sources of

inefficiencies within the farm sector, (better) functioning markets for land and capital as

well as on the farm output side may provide major incentives to reallocate resources

and boost farm efficiency and productivity. On the other hand, there might be adverse

conditions as climate change that will affect the future prospect of crop production and

even might decrease yield levels (Mueller et al. 2016).

http://www.dict.cc/englisch-deutsch/negligible.html

42

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45

Appendix A. Summary statistics of variables for DEA model, 2008-2013

Variables Obs Mean Std. Dev. Min Max

DEA model variables - unbalanced panel

Climatic zone 1

CP value (tsd UAH) 7370 5443 13867 14 295482

Labor units in CP (persons) 7370 32 56 1 1309

Total land (ha) 7370 1636 2895 6 90621

Material costs and depreciations in CP (tsd UAH) 7370 3957 11575 4 424671

From that – depreciation 7370 400 1257 0 30322

- material cost (without services) 7370 3557 10768 2 418044

- services 7370 947 4551 0 233980

Climatic zone 2

CP value (tsd UAH) 13276 8343 25110 17 824351


Total land (ha) 13276 2365 5601 4 178083




- services 13276 1143 4299 0 127464

Climatic zone 3

CP value (tsd UAH) 24255 5314 10434 19 370905


Total land (ha) 24255 2210 3630 8 136400




- services 24255 574 1558 0 64274

DEA model variables - balanced panel

Climatic zone 1

CP value (tsd UAH) 3360 7056 14303 59 237306


Total land (ha) 3360 2023 2847 40 40503




- services 3360 1099 4313 0 119675

Climatic zone 2

CP value (tsd UAH) 7949 9188 24593 99 603044


Total land (ha) 7949 2558 5625 81 178083



46

Variables Obs Mean Std. Dev. Min Max


- services 7949 1139 3614 0 126824

Climatic zone 3

CP value (tsd UAH) 15673 6320 11776 76 370905


Total land (ha) 15673 2578 4177 60 136400




- services 15673 629 1680 0 64274

Note: Monetary values are expressed in nominal values.


Appendix B. Summary statistics of variables for DEA model for individual years, 2008-2013

2008 2009 2010 2011 2012 2013

Variables Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

DEA model variables - unbalanced panel

Climatic zone 1 CP value (tsd UAH)

1462 3858 7705 1347 3919 9057 1156 4335 9984 1196 5845 16067 1119 7440 17266 1090 8140 20150

Labor units in CP (persons)

1462 33 43 1347 30 42 1156 33 53 1196 31 55 1119 33 63 1090 35 79

Total land (ha)

1462 1314 1797 1347 1417 2078 1156 1641 2510 1196 1678 3078 1119 1865 3213 1090 2053 4351

Material costs and depreciations in CP (tsd UAH)

1462 2447 5070 1347 2450 6243 1156 3152 8018 1196 4216 11442 1119 5671 14816 1090 6655 19230

From that - depreciation

1462 172 728 1347 299 791 1156 416 1179 1196 422 1044 1119 562 1599 1090 626 1937

- material cost (without services)

1462 2275 4640 1347 2152 5694 1156 2736 7231 1196 3794 10818 1119 5109 13725 1090 6029 18010

- services 1462 442 1477 1347 514 2391 1156 710 2919 1196 1071 4676 1119 1425 4975 1090 1787 8376


2314 6928 18335 2248 7208 22603 2170 6949 21787 2166 8866 27424 2185 9718 26675 2193 10493 31726


2314 53 135 2248 48 103 2170 51 132 2166 49 129 2185 51 148 2193 49 119

Total land (ha)

2314 2199 5198 2248 2258 4922 2170 2427 6169 2166 2365 5597 2185 2458 5740 2193 2496 5928


2314 4152 12152 2248 3965 11119 2170 4368 13647 2166 5287 15680 2185 6440 18119 2193 7264 20696


2314 259 723 2248 496 2039 2170 503 1265 2166 595 1639 2185 700 1952 2193 749 2221


2314 3892 11605

2248 3468 9687 2170

3865 12757

2166 4692 14461 2185 5741 16688 2193 6515 19254

48

2008 2009 2010 2011 2012 2013


Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

- services 2314 867 3747 2248 811 2894 2170 979 4079 2166 1222 4846 2185 1415 4398 2193 1587 5392


3883 5419 10123 3970 4881 9252 4115 4587 8598 4247 5563 10907 4022 5236 11116 4018 6201 12124


3883 44 99 3970 40 89 4115 37 82 4247 36 77 4022 35 63 4018 33 64

Total land (ha)

3883 2314 3692 3970 2279 3708 4115 2199 3602 4247 2140 3631 4022 2170 3573 4018 2166 3575


3883 2965 5896 3970 2683 5636 4115 2650 5204 4247 3198 5955 4022 3677 7101 4018 4195 7318


3883 255 612 3970 431 974 4115 427 965 4247 477 1004 4022 544 1279 4018 562 1226


3883 2709 5372 3970 2252 4811 4115 2222 4388 4247 2721 5141 4022 3132 6174 4018 3633 6309

- services 3883 504 1418 3970 462 1389 4115 491 1327 4247 577 1306 4022 622 1925 4018 789 1847

DEA model variables - balanced panel


560 6165 9766 560 6030 10819 560 5407 9198 560 7652 15885 560 8476 17410 560 8605 19169


560 45 50 560 42 50 560 41 54 560 42 63 560 42 71 560 40 64

Total land (ha)

560 1842 2105 560 1950 2418 560 2005 2626 560 2097 3117 560 2110 3204 560 2135 3391


560 3756 6339 560 3708 7733 560 3920 8406 560 5309 12449 560 6333 15797 560 6465 13468


560 275 560 560 477 980 560 537 1195 560 580 1219 560 648 1545 560 651 1736


560 3481 5892 560 3231 7137 560 3383 7730 560 4729 1169

9 560 5686 14845 560 5814 12029

- services 560 694 2086 560 825 3551 560 844 3274 560 1317 5843 560 1549 6086 560 1365 3504

49

2008 2009 2010 2011 2012 2013


Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.

Obs Mean Std. Dev.


1325 7996 19880 1325 8191 23900 1325 7402 19049 1325 9998 27851 1325 10524 26523 1325 11016 28486


1325 60 156 1325 54 118 1325 55 118 1325 56 127 1325 57 128 1325 56 122

Total land (ha)

1325 2446 6029 1325 2496 5513 1325 2555 5764 1325 2609 5546 1325 2617 5387 1325 2622 5496

Material costs and

depreciations in CP (tsd UAH)

1325 4551 11197 1325 4506 12175 1325 4528 11175 1325 5832 15287 1325 6890 17843 1325 7620 19569


1325 308 769 1325 592 2473 1325 537 1121 1325 668 1739 1325 763 1962 1325 847 2536


1325 4242 10586 1325 3913 10410 1325 3991 10513 1325 5165 13932 1325 6126 16391 1325 6773 17701

- services 1325 892 3111 1325 868 2951 1325 954 2987 1325 1280 4575 1325 1312 3481 1325 1530 4204

Climatic zone 3 CP value (tsd

UAH) 2612 6256 11216 2612 5722 10418 2612 5599 9819 2612 6942 12347 2612 6181 12496 2612 7220 13812


2612 49 111 2612 46 101 2612 45 96 2612 45 92 2612 42 71 2612 41 75

Total land (ha)

2612 2587 4229 2612 2605 4274 2612 2608 4207 2612 2591 4157 2612 2560 4118 2612 2517 4075


2612 3349 6610 2612 3100 6401 2612 3197 5986 2612 3923 6680 2612 4310 7974 2612 4809 7902

From that -depreciation

2612 296 694 2612 509 1079 2612 529 1089 2612 612 1116 2612 659 1450 2612 675 1350


2612 3052 6005 2612 2591 5482 2612 2668 5063 2612 3311 5761 2612 3651 6958 2612 4134 6763

- services 2612 548 1597 2612 502 1577 2612 566 1526 2612 652 1388 2612 683 2121 2612 822 1756

Note: Monetary values are expressed in nominal values.


______________________________________________________________ANNEX TO CHAPTER 6

50

Appendix C. Total factor productivity change, 2008-2013

Variable 2008~2009 2009~2010 2010~2011 2011~2012 2012~2013 Cumulative

Climatic zone 1

TFP change 1.060 1.108 0.894 0.990 1.028 1.069

Technical

change 0.954 1.181 0.812 1.285 0.846 0.995

Efficiency change

1.111 0.938 1.101 0.771 1.215 1.075

Pure efficiency

change 1.078 0.911 1.082 0.835 1.219 1.082

Scale efficiency

change 1.031 1.030 1.018 0.923 0.997 0.995

Climatic zone 2

TFP change 1.014 1.104 0.864 1.000 1.003 0.970

Technical

change 1.006 1.050 0.912 1.079 1.078 1.121

Efficiency

change 1.008 1.052 0.947 0.927 0.930 0.866

Pure efficiency change

0.999 1.066 0.925 0.922 0.964 0.876

Scale efficiency

change 1.009 0.986 1.024 1.005 0.965 0.988

Climatic zone 3

TFP change 1.019 1.039 0.940 1.207 0.901 1.082

Technical change

1.123 1.016 0.914 1.175 0.888 1.088

Efficiency

change 0.907 1.022 1.029 1.027 1.015 0.994

Pure efficiency

change 0.943 1.015 1.037 1.029 1.021 1.043

Scale efficiency change

0.962 1.007 0.992 0.999 0.994 0.954

Note: The values are geometric means of individual farm values.

Number of observations: CZ1 - 560, CZ2 - 1324, CZ3 - 2612 (numbers are equal for the same

climatic zone across years).

efficiency and profitability of ukrainian crop …...at the institute for economic research and...

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